A major limitation of genetic testing for BRCA mutations using sequence-based approaches is that missense mutations of uncertain clinical significance are frequently identified. Despite the identification of such missense mutations in 13-17% of individuals who undergone genetic testing, there is no information regarding the biologic significance of these changes in the majority of these cases to assist in guiding clinical management. In order to address this issue, we propose to conduct a combined structural, computational and epidemiologic analysis of BRCA2 missense mutations. Briefly, we plan to develop and refine a computational protocol to incorporate structural modeling and protein superfamily analysis to predict the biologic significance of specific BRCA2 missense mutations. We will then use this computational protocol to gain insight into the functional importance of BRCA2 missense mutations that have been frequently reported to the Breast Cancer Information Core database. Missense mutations predicted by the computational analysis to likely be functionally significant will then be analyzed in association and cosegregation studies in an attempt to further elucidate the functional significance of these mutations. The combined approach used in this proposal will build upon identified strengths at our institution in Structural and Computational Biology, Genetic Epidemiology, and Clinical Genetics. This combined approach we believe will allow us to develop what we hope to be a more powerful and biologically relevant method of analyzing BRCA2 variants of uncertain significance, which if confirmed, will be directly and immediately translatable to individuals and families with these mutations.